Selective zkML Proofs: Verifying High-Risk AI Model Slices for Privacy-Preserving Inference
In the high-stakes world of financial modeling and healthcare diagnostics, AI inferences carry immense responsibility. A single erroneous output from...
Research Papers & Resources
zkML for Privacy-Preserving LLM Fine-Tuning: Zero-Knowledge Proofs in Federated Pipelines
In the rush to harness large language models for specialized tasks, organizations grapple with a stark reality: fine-tuning these behemoths...
zkML Consensus Mechanisms Multi-Model Evaluation Mira Warden Protocol
In the evolving landscape of decentralized AI, zkML consensus mechanisms stand out as a prudent safeguard against the inherent uncertainties...
Scaling zkML Proofs for Large Models Inference Labs Sharding Approach
In the evolving landscape of artificial intelligence, where models balloon to hundreds of billions of parameters, verifying computations without exposing...
